Blind Modulation Recognition Algorithm for MIMO-OFDM Signal Based on CNN-LSTM
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Abstract
As one of the core technologies of non-cooperative communication, blind modulation recognition technology for wireless communication signals plays a crucial role in improving spectrum utilization efficiency and the demodulation of unknown signals. In addition, non-cooperative communication experiences problems such as an unknown electromagnetic environment, serious noise interference, and a low signal-to-noise ratio, which make it difficult to blindly modulate and identify unknown signals. In order to solve the problem of subcarrier blind modulation recognition of multiple-input multiple-output orthogonal frequency division multiplexing signals in non-cooperative communication at a low signal-to-noise ratio, this study used a convolutional neural network (CNN) and long short-term memory (LSTM) network to build a one-dimensional CNN-LSTM network for blind modulation identification. Because of the strong feature-expression ability of I/Q data, the algorithm used I/Q data as the first input feature and directly entered it into the network. In order to compensate for the interference of noise on I/Q data, a cyclic spectrum with strong noise immunity was also selected as another input feature. In order to further improve the noise immunity of the cyclic spectrum, a cyclic spectrum slice accumulation sequence with better noise immunity was used as the second input feature. Simulation results showed that the proposed method could recognize the {BPSK, QPSK, 8PSK, 16QAM, 32QAM, 128QAM} modulation mode under a signal-to-noise ratio of 2 dB, and the recognition accuracy reached 98%.
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